%!TEX root = main.tex

\makeatletter{}

\begin{abstract}
Several recent works have focused on harvesting HTML tables from the 
Web and recovering their 
semantics~\cite{cafarella2008webtables,elmeleegy2009harvesting,limaye2010annotating,venetis2011recovering}. 
As a result, hundreds of millions of high quality structured data tables can now be explored by the users. In this paper, we argue that 
those efforts only scratch the surface of the true value of structured data on the Web, 
and study the challenging problem of {\em synthesizing tables from the Web}, i.e., 
producing {\em never-before-seen} tables from raw tables on the Web. Table synthesis 
offers an important semantic advantage: when a set of related tables are combined into a 
single union table, powerful mechanisms, such as temporal or geographical comparison and 
visualization, can be employed to understand and mine the underlying data holistically.

%First, many raw tables are too small or semantically narrow to be considered
%valuable on their own, but provide tremendous value when combined together
%with other similar tables. 

We focus on one fundamental task of table synthesis, namely, {\em table stitching}.  Within a given site, 
many tables with {\em identical} schemas can be scattered across many pages.  The task of table stitching 
involves combining such tables into a single meaningful {\em union} table and identifying extra attributes 
and values for its rows so that rows from different original tables can be distinguished. Specifically, we 
first define the notion of ``stitchable'' tables and identify collections of tables that can be stitched. 
Second, we design an effective algorithm for extracting hidden attributes that are essential for the 
stitching process and for aligning values of those attributes across tables to synthesize new columns. We 
also assign meaningful names to these synthesized columns. Experiments on real world tables demonstrate the 
effectiveness of our approach.
\end{abstract}

%Despite the success of extracting a large number of tables as separate (mini-)databases, 
%the relationship between them is vastly ignored in the literature 
%except~\cite{das2012finding} that studied how to find related tables. In this paper, we 
%will present the first attempt, as far as we are aware of, of providing a unified view of 
%a set of individual tables. Specifically, we identify tables that are highly relevant, 
%infer the meta-data from their context, and finally merge them into a synthesized table 
%with semantic consistency. The whole process is called {\em Table Stitching} where the 
%meta-data is the threads that hold all the tables together. We describe an experiment 
%that demonstrates the effectiveness of our proposed method to provide useful metadata for 
%stitching. We also show that we can automatically label the extracted metadata with 
%datatypes with reasonable performance. We believe this will enable users to explore the 
%harvested databases with more powerful capabilities.


%\begin{abstract}
%Recently, there has been quite much work on harvesting raw HTML tables from the Web \cite{cafarella2008webtables,cafarella2008uncovering,venetis2011recovering} (or HTML lists \cite{elmeleegy2009harvesting}). Despite the success of extracting a large number of tables as separate (mini-)databases, the relationship between them is vastly ignored in the literature. In this paper, we will present the first attempt, to our best knowledge, of providing a structural view of a set of individual tables. Specifically, we identify tables that are highly relevant, infer the metadata\footnote{In this paper, we will overload the term \emph{metadata} to the implicit values that can serve as additional columns and are true for all the tuples in the table. Assume that the tables from a group $G$ are conceivably from the same table $T_G$ but are projected out according to some selection conditions. In other words, each table in the previous example could be represented as ``\texttt{SELECT} \emph{town,school,\#students,T:S} \texttt{FROM} $T_G$ \texttt{WHERE} $m^1$=\emph{area} \texttt{and} $m^2$=\emph{school\_type}'' by varying the value of \emph{area} and \emph{school\_type}. Here, \emph{area} and \emph{school\_type} are two metadata columns. The term \emph{context} is accordingly referred to the pieces of text where the metadata is present.} from their context, and finally merge them into a synthesized table with semantic consistency. The whole process is called {\em Table Stitching} where the extracted meta-data is the threads that hold all the tables together. We describe an experiment that demonstrates the effectiveness of our proposed method to provide useful metadata for stitching. We also show that we can automatically label the extracted metadata with datatypes with reasonable performance. We believe this will enable users to explore the harvested databases with more powerful capabilities.



%Most table extraction works have been focusing on extracting html tables from a single web document. Often times tables from different pages (within the same site/domain) are selectional views of the same underlying comprehensive big table. Stitching these sub-tables across pages with recovered selection conditions is critical for many importatn applications like table search. We study the ``table-stitching'' problem in this paper, which synthesize relational data tables across web documents with additional information besides the tables content. 
%\end{abstract}
